package sparkStream

import org.apache.kafka.clients.producer.{KafkaProducer, ProducerConfig, ProducerRecord}
import org.apache.kafka.common.serialization.StringDeserializer
import org.apache.spark.SparkConf
import org.apache.spark.streaming.kafka010.KafkaUtils
import org.apache.spark.streaming.kafka010.LocationStrategies.PreferConsistent
import org.apache.spark.streaming.{Seconds, StreamingContext}
import org.apache.spark.streaming.kafka010.ConsumerStrategies.Subscribe

import java.util.HashMap





object SparkKafka {
  def main(args: Array[String]): Unit = {
    /* spark streaming实现kafka的消费者
    1）构建sparkconf 本地运行，运行应用程序名称
    2）构建sparkstreaming ————》  streamingContext，加载配置
    3）kafka 配置 brokee，key value ，group id，消费模式
    4）spark 链接kafka 订阅，设置topic名字和策略，streamingcontext
    5）循环的形式 打印
    6）开启sparkstreamingcontext，监控
     */

//
//    1）构建sparkconf 本地运行，运行应用程序名称
    val conf =new SparkConf().setMaster("local[*]").setAppName("helloSparkKafka")

    val ssc = new StreamingContext(conf,Seconds(2))

    //spark 输出红色info信息  --》error
    ssc.sparkContext.setLogLevel("error")


//    3）kafka 配置 broker，key value ，group id，消费模式
    val kfkaParms = Map[String,Object](
      "bootstrap.servers"-> "123.56.187.176:9092",
      "key.deserializer"->classOf[StringDeserializer],
      "value.deserializer"->classOf[StringDeserializer],
      "group.id"->"niit",
      "enable.auto.commit"->(false:java.lang.Boolean)
    )
//    4）spark 链接kafka 订阅，设置topic名字和策略，streamingcontext
      //topic name
      val topicName = Array("stuInfo")
//      val topicName = Array("15test")
      val streamRdd = KafkaUtils.createDirectStream[String,String](
        ssc,
        PreferConsistent,
        Subscribe[String,String](topicName,kfkaParms)
      )

    //  producer 配置项
    val property = new HashMap[String,Object]()
    property.put(ProducerConfig.BOOTSTRAP_SERVERS_CONFIG, "192.168.23.128:9092")
    property.put(ProducerConfig.KEY_SERIALIZER_CLASS_CONFIG, "org.apache.kafka.common.serialization.StringSerializer")
    property.put(ProducerConfig.VALUE_SERIALIZER_CLASS_CONFIG, "org.apache.kafka.common.serialization.StringSerializer")


    //时间窗口 1
    //streamRdd kafka 返回的数据 key，value 数据是value
    val res = streamRdd.map(_.value())
    val result=res.flatMap(_.split("\t")).map((_,1)).reduceByKeyAndWindow(_+_,Seconds(4),Seconds(4))
    result.foreachRDD(
      x=>{
        println("-------数据是-------")
        x.foreach(
        obj=>{
          println(obj)

          val  producer =new KafkaProducer[String,String](property)
          producer.send(new ProducerRecord[String,String]("15homework",obj.toString))
          producer.close()
        }
        )
      }
    )




//        streamRdd.foreachRDD(
//          x=>{
//            if (! x.isEmpty()){
//              val line =x.map(_.value())  //匿名函数
//              line.foreach(println)
//            }
//          }
//        )
//

    ssc.start()
    ssc.awaitTermination()



  }
}
